{"type":"ScholarlyArticle","title":"Optimizing Crop Management with Reinforcement Learning and Imitation Learning","authors":[{"name":"Ran Tao","slug":"ran-tao","given":"Ran","family":"Tao","affiliation":"University of Illinois at Urbana-Champaign"},{"name":"Pan Zhao","slug":"pan-zhao","given":"Pan","family":"Zhao","affiliation":"University of Illinois at Urbana-Champaign"},{"name":"Jing Wu","slug":"jing-wu","given":"Jing","family":"Wu","affiliation":"University of Illinois at Urbana-Champaign"},{"name":"Nicolas Martin","slug":"nicolas-martin","given":"Nicolas","family":"Martin","affiliation":"University of Illinois at Urbana-Champaign"},{"name":"Matthew T. Harrison","slug":"matthew-t-harrison","given":"Matthew T.","family":"Harrison","affiliation":"University of Tasmania"},{"name":"Carla Ferreira","slug":"carla-ferreira","given":"Carla","family":"Ferreira","affiliation":"Stockholm University"},{"name":"Zahra Kalantari","slug":"zahra-kalantari","given":"Zahra","family":"Kalantari","affiliation":"KTH Royal Institute of Technology"},{"name":"Naira Hovakimyan","slug":"naira-hovakimyan","given":"Naira","family":"Hovakimyan","affiliation":"University of Illinois at Urbana-Champaign"}],"abstract":"Crop management has a significant impact on crop yield, economic profit, and the environment. Although management guidelines exist, finding the optimal management practices is challenging. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system that optimizes nitrogen fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require a large number of state variables from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a few state variables that can be easily obtained or measured in the real world (denoted as partial observation) by mimicking the actions of the RL policies trained under full observation. Simulation experiments using the maize crop in Florida (US) and Zaragoza (Spain) demonstrate that the trained policies from both RL and IL techniques achieved more than 45\\% improvement in economic profit while causing less environmental impact compared with a baseline method. Most importantly, the IL-trained management policies are directly deployable in the real world as they use readily available information.","doi":"10.24963/ijcai.2023/691","url":"https://ijcai.org/proceedings/2023/691","pdf":"https://ijcai.org/proceedings/2023/0691.pdf","markdown":"https://ijcai.org/proceedings/2023/691.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/691","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":691,"pages":{"first":6228,"last":6236},"keywords":["AI for Good: Multidisciplinary Topics and Applications","AI for Good: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Ara et al., 2021] Iffat Ara, Lydia Turner, Matthew Tom Har- rison, Marta Monjardino, Peter DeVoil, and Daniel Ro- driguez. Application, adoption and opportunities for im- proving decision support systems in irrigated agriculture: A review. Agricultural Water Management, 257:107161, 2021.","title":"Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review","authors":"Iffat Ara, Lydia Turner, Matthew Tom Harrison, Marta Monjardino, Peter Devoil, Daniel Rodriguez","year":"2021","venue":"Agricultural Water Management","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ashcraft and Karra, 2021] Chace Ashcraft and Kiran Karra. Machine learning aided crop yield optimization. arXiv preprint arXiv:2111.00963, 2021.","title":"Machine learning aided crop yield optimization","authors":"Karra Ashcraft, Chace Ashcraft, Kiran Karra","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Asseng et al., 2013] Senthold Asseng, Frank Ewert, Cyn- thia Rosenzweig, James W Jones, Jerry L Hatfield, Alex C Ruane, Kenneth J Boote, Peter J Thorburn, Reimund P Rötter, Davide Cammarano, et al. Uncertainty in simu- lating wheat yields under climate change. Nature climate change, 3(9):827-832, 2013.","title":"Uncertainty in simulating wheat yields under climate change","authors":"Asseng","year":"2013","venue":"Nature climate change","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Attonaty et al., 1997] J-M Attonaty, M-H Chatelin, F Gar- cia, and S Ndiaye. Using extended machine learning and simulation technics to design crop management strate- gies. In EFITA First European Conference for Information Technology in Agriculture, Copenhagen (Denmark), 1997.","title":"Using extended machine learning and simulation technics to design crop management strategies","authors":"J-M Attonaty, M-H Chatelin, F Garcia, Ndiaye","year":"1997","venue":"EFITA First European Conference for Information Technology in Agriculture","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bassu et al., 2014] Simona Bassu, Nadine Brisson, Jean- Louis Durand, Kenneth Boote, Jon Lizaso, James W Jones, Cynthia Rosenzweig, Alex C Ruane, Myriam Adam, Christian Baron, et al. How do various maize crop models vary in their responses to climate change factors? Global change biology, 20(7):2301-2320, 2014.","title":"How do various maize crop models vary in their responses to climate change factors?","authors":"Simona Bassu, Nadine Brisson, Jean-Louis Durand, Kenneth Boote, Jon Lizaso, James W Jones, Cynthia Rosenzweig, Alex C Ruane, Myriam Adam, Christian Baron","year":"2014","venue":"Global change biology","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Brockman et al., 2016] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.","title":"Openai gym","authors":"Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cobos and Devices, 2010] Douglas R Cobos and Decagon Devices. Why does my soil moisture sensor read neg- ative? Online]. Available: http://manuals. decagon. com/RetiredandDiscontinued/Slicksandcontent/ Presenta- tions/SoilMoisture301.pdf, 2010.","title":"Why does my soil moisture sensor read negative? Online","authors":"Decagon Douglas R Cobos, Devices","year":"2010","venue":null,"doi":null,"url":"http://manuals.decagon.com/RetiredandDiscontinued/Slicksandcontent/Presenta-tions/SoilMoisture301.pdf","internal_url":null},{"position":8,"raw":"[Di and Cameron, 2002] HJ Di and KC Cameron. Nitrate leaching in temperate agroecosystems: sources, factors and mitigating strategies. Nutrient cycling in agroecosys- tems, 64:237-256, 2002.","title":"Nitrate leaching in temperate agroecosystems: sources, factors and mitigating strategies","authors":"Cameron ; ] Hj Di, K C Di, Cameron","year":"2002","venue":"Nutrient cycling in agroecosystems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Floehr, 2010] Eric Floehr. Weather forecast accuracy anal- ysis. In Proc of the 9th Python in Science Conference, SciPy, pages 36-39, 2010.","title":"Weather forecast accuracy analysis","authors":"Eric Floehr","year":"2010","venue":"Proc of the 9th Python in Science Conference, SciPy","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Garcia, 1999] Frédérick Garcia. Use of reinforcement learning and simulation to optimize wheat crop techni- cal management. In Proceedings of the International Congress on Modelling and Simulation, pages 801-806, 1999.","title":"Use of reinforcement learning and simulation to optimize wheat crop technical management","authors":"Frédérick Garcia, Garcia","year":"1999","venue":"Proceedings of the International Congress on Modelling and Simulation","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gautron et al., 2022] Romain Gautron, Odalric-Ambrym Maillard, Philippe Preux, Marc Corbeels, and Régis Sab- badin. Reinforcement learning for crop management sup- port: Review, prospects and challenges. Computers and Electronics in Agriculture, 200:107182, 2022.","title":"Reinforcement learning for crop management support: Review, prospects and challenges","authors":"Romain Gautron, Odalric-Ambrym Maillard, Philippe Preux, Marc Corbeels, Régis Sab-Badin","year":"2022","venue":"Computers and Electronics in Agriculture","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Heinemann et al., 2006] Detlev Heinemann, Elke Lorenz, and Marco Girodo. Forecasting of solar radiation. So- lar energy resource management for electricity generation from local level to global scale. Nova Science Publishers, New York, pages 83-94, 2006.","title":"Forecasting of solar radiation. Solar energy resource management for electricity generation from local level to global scale","authors":"Detlev Heinemann, Elke Lorenz, Marco Girodo","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hussein et al., 2017] Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne. Imitation learn- ing: A survey of learning methods. ACM Computing Sur- veys (CSUR), 50(2):1-35, 2017.","title":"Imitation learning: A survey of learning methods","authors":"Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, Chrisina Jayne","year":"2017","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jones et al., 2003] James W Jones, Gerrit Hoogenboom, Cheryl H Porter, Ken J Boote, William D Batchelor, LA Hunt, Paul W Wilkens, Upendra Singh, Arjan J Gi- jsman, and Joe T Ritchie. The DSSAT cropping system model. European Journal of Agronomy, 18(3-4):235-265, 2003.","title":"The DSSAT cropping system model","authors":"James W Jones, Gerrit Hoogenboom, Cheryl H Porter, Ken J Boote, William D Batchelor, L A Hunt, Paul W Wilkens, Upendra Singh, Arjan J Gijsman, Joe T Ritchie","year":"2003","venue":"European Journal of Agronomy","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jones et al., 2017] James W Jones, John M Antle, Bruno Basso, Kenneth J Boote, Richard T Conant, Ian Foster, H Charles J Godfray, Mario Herrero, Richard E Howitt, Sander Janssen, et al. Brief history of agricultural systems modeling. Agricultural systems, 155:240-254, 2017.","title":"Brief history of agricultural systems modeling","authors":"James W Jones, John M Antle, Bruno Basso, Kenneth J Boote, Richard T Conant, Ian Foster, H Charles, J Godfray, Mario Herrero, Richard E Howitt, Sander Janssen","year":"2017","venue":"Agricultural systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kaufmann et al., 2018] Elia Kaufmann, Antonio Loquer- cio, Rene Ranftl, Alexey Dosovitskiy, Vladlen Koltun, and Davide Scaramuzza. Deep drone racing: Learning agile flight in dynamic environments. In Conference on Robot Learning, pages 133-145, 2018.","title":"Deep drone racing: Learning agile flight in dynamic environments","authors":"Elia Kaufmann, Antonio Loquercio, Rene Ranftl, Alexey Dosovitskiy, Vladlen Koltun, Davide Scaramuzza","year":"2018","venue":"Conference on Robot Learning","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Malik et al., 2019] Wafa Malik, Ramon Isla, and Farida Dechmi. Dssat-ceres-maize modelling to improve irriga- tion and nitrogen management practices under mediter- ranean conditions. Agricultural Water Management, 213:298-308, 2019.","title":"Dssat-ceres-maize modelling to improve irrigation and nitrogen management practices under mediterranean conditions","authors":"Wafa Malik, Ramon Isla, Farida Dechmi","year":"2019","venue":"Agricultural Water Management","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mandrini et al., 2022] German Mandrini, Cameron Mark Pittelkow, Sotirios Archontoulis, David Kanter, and Nico- las F Martin. Exploring trade-offs between profit, yield, and the environmental footprint of potential nitrogen fer- tilizer regulations in the us midwest. Frontiers in plant science, 13, 2022.","title":"Exploring trade-offs between profit, yield, and the environmental footprint of potential nitrogen fertilizer regulations in the us midwest","authors":"Mandrini","year":"2022","venue":"Frontiers in plant science","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Mnih et al., 2015] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al. Human-level control through deep re- inforcement learning. Nature, 518(7540):529-533, 2015.","title":"Human-level control through deep reinforcement learning","authors":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Muskardin et al., 2022] Edi Muskardin, Martin Tappler, Bernhard K Aichernig, and Ingo Pill. Reinforcement learning under partial observability guided by learned environment models. arXiv preprint arXiv:2206.11708, 2022.","title":"Reinforcement learning under partial observability guided by learned environment models","authors":"Edi Muskardin, Martin Tappler, Bernhard K Aichernig, Ingo Pill","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Osa et al., 2018] Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al. An algorithmic perspective on imitation learn- ing. Foundations and Trends® in Robotics, 7(1-2):1-179, 2018.","title":"An algorithmic perspective on imitation learning","authors":"Takayuki Osa, Joni Pajarinen, Gerhard Neumann, Andrew Bagnell, Pieter Abbeel, Jan Peters","year":"2018","venue":"Foundations and Trends® in Robotics","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Overweg et al., 2021] Hiske Overweg, Herman NC Berghuijs, and Ioannis N Athanasiadis. Cropgym: a re- inforcement learning environment for crop management. arXiv preprint arXiv:2104.04326, 2021.","title":"Cropgym: a reinforcement learning environment for crop management","authors":"Overweg","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Palmer et al., 2013] Mike W Palmer, Julia Cooper, Cather- ine Tétard-Jones, et al. The influence of organic and con- ventional fertilisation and crop protection practices, pre- ceding crop, harvest year and weather conditions on yield and quality of potato (Solanum tuberosum) in a long-term management trial. European Journal of Agronomy, 49:83- 92, 2013.","title":"The influence of organic and conventional fertilisation and crop protection practices, preceding crop, harvest year and weather conditions on yield and quality of potato (Solanum tuberosum) in a long-term management trial","authors":"Mike W Palmer, Julia Cooper, Catherine Tétard-Jones","year":"2013","venue":"European Journal of Agronomy","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Peng et al., 2018] Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel. Sim-to-real trans- fer of robotic control with dynamics randomization. In IEEE International Conference on Robotics and Automa- tion (ICRA), pages 3803-3810, 2018.","title":"Sim-to-real transfer of robotic control with dynamics randomization","authors":"Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel","year":"2018","venue":"IEEE International Conference on Robotics and Automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Reddy et al., 2003] BVS Reddy, P Sanjana Reddy, F Bidinger, and Michael Blümmel. Crop management factors influencing yield and quality of crop residues. Field Crops Research, 84(1-2):57-77, 2003.","title":"Crop management factors influencing yield and quality of crop residues","authors":"Reddy","year":"2003","venue":"Field Crops Research","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Romain et al., 2022] Gautron Romain, Preux Philippe, Bigot Julien, Maillard Odalric-Ambrym, Emukpere David, et al. gym-dssat: a crop model turned into a reinforcement learning environment. arXiv preprint arXiv:2207.03270, 2022.","title":"gym-dssat: a crop model turned into a reinforcement learning environment","authors":"Romain","year":"2022","venue":"Gautron Romain","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Searchinger et al., 2019] Tim Searchinger, Richard Waite, Craig Hanson, Janet Ranganathan, Patrice Dumas, Emily Matthews, and Carni Klirs. Creating a sustainable food fu- ture: A menu of solutions to feed nearly 10 billion people by 2050. final report. World Resources Institude, 2019.","title":"Creating a sustainable food future: A menu of solutions to feed nearly 10 billion people by 2050","authors":"Tim Searchinger, Richard Waite, Craig Hanson, Janet Ranganathan, Patrice Dumas, Emily Matthews, Carni Klirs","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Skhiri and Dechmi, 2012] Ahmed Skhiri and Farida Dechmi. Impact of sprinkler irrigation management on the del reguero river (spain). i: Water balance and irrigation performance. Agricultural Water Management, 103:120-129, 2012.","title":"Impact of sprinkler irrigation management on the del reguero river (spain). i: Water balance and irrigation performance","authors":"Ahmed Skhiri, Farida Dechmi","year":"2012","venue":"Agricultural Water Management","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Sun et al., 2017] Lijia Sun, Yanxiang Yang, Jiang Hu, Dana Porter, Thomas Marek, and Charles Hillyer. Reinforce- ment learning control for water-efficient agricultural irri- gation. In 2017 IEEE International Symposium on Paral- lel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Comput- ing and Communications (ISPA/IUCC), pages 1334-1341, 2017.","title":"Reinforcement learning control for water-efficient agricultural irrigation","authors":"Lijia Sun, Yanxiang Yang, Jiang Hu, Dana Porter, Thomas Marek, Charles Hillyer","year":"2017","venue":"IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Tobin et al., 2017] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural net- works from simulation to the real world. In IEEE/RSJ In- ternational Conference on Intelligent Robots and Systems (IROS), pages 23-30, 2017.","title":"Domain randomization for transferring deep neural networks from simulation to the real world","authors":"Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel","year":"2017","venue":"IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wright et al., 2022] David Wright, Ian Small, Cheryl Mackowiak, Zane Grabau, Pratap Devkota, and Silvana Paula-Moraes. Field corn production guide: Ss-agr- 85/ag202, rev. 8/2022. EDIS, 2022(4), 2022.","title":"Field corn production guide","authors":"David Wright, Ian Small, Cheryl Mackowiak, Zane Grabau, Pratap Devkota, Silvana Paula-Moraes","year":"2022","venue":"EDIS","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wu et al., 2022] Jing Wu, Ran Tao, Pan Zhao, Nicolas F Martin, and Naira Hovakimyan. Optimizing nitrogen man- agement with deep reinforcement learning and crop sim- ulations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 1712-1720, 2022.","title":"Optimizing nitrogen man-agement with deep reinforcement learning and crop simulations","authors":"Jing Wu, Ran Tao, Pan Zhao, Nicolas F Martin, Naira Hovakimyan","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wu et al., 2023] Jing Wu, David Pichler, Daniel Marley, David Wilson, Naira Hovakimyan, and Jennifer Hobbs. Extended agriculture-vision: An extension of a large aerial image dataset for agricultural pattern analysis. arXiv preprint arXiv:2303.02460, 2023.","title":"Extended agriculture-vision: An extension of a large aerial image dataset for agricultural pattern analysis","authors":"Jing Wu, David Pichler, Daniel Marley, David Wilson, Naira Hovakimyan, Jennifer Hobbs","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhang and Cho, 2016] Jiakai Zhang and Kyunghyun Cho. Query-efficient imitation learning for end-to-end au- tonomous driving. arXiv preprint arXiv:1605.06450, 2016.","title":"Query-efficient imitation learning for end-to-end autonomous driving","authors":"Cho Zhang, Jiakai Zhang, Kyunghyun Cho","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang et al., 2016] Tianhao Zhang, Gregory Kahn, Sergey Levine, and Pieter Abbeel. Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search. In 2016 IEEE international conference on robotics and automation (ICRA), pages 528-535. IEEE, 2016.","title":"Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search","authors":"Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel","year":"2016","venue":"IEEE international conference on robotics and automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2018] Pengcheng Zhang, Yangyang Jia, Jerry Gao, Wei Song, and Hareton Leung. Short-term rainfall forecasting using multi-layer perceptron. IEEE Transac- tions on Big Data, 6(1):93-106, 2018.","title":"Short-term rainfall forecasting using multi-layer perceptron","authors":"Pengcheng Zhang, Yangyang Jia, Jerry Gao, Wei Song, Hareton Leung","year":"2018","venue":"IEEE Transactions on Big Data","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhao et al., 2019] Chuang Zhao, Bing Liu, Liujun Xiao, Gerrit Hoogenboom, Kenneth J Boote, Belay T Kassie, Willingthon Pavan, Vakhtang Shelia, Kwang Soo Kim, Ix- chel M Hernandez-Ochoa, et al. A simple crop model. European Journal of Agronomy, 104:97-106, 2019.","title":"A simple crop model","authors":"Chuang Zhao, Bing Liu, Liujun Xiao, Gerrit Hoogenboom, Kenneth J Boote, Willingthon Belay T Kassie, Vakhtang Pavan, Kwang Shelia, Soo Kim, M Ixchel, Hernandez-Ochoa","year":"2019","venue":"European Journal of Agronomy","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}